用因果预测与生成技术优化销售流程，提升企业转化效率。
Causal Predictive Optimization and Generation for Business AI
- 基于因果机器学习预测销售结果，避免传统模型偏差
- 融合约束优化与上下文强化学习，动态调整销售策略
- 生成式AI+反馈循环实现系统持续进化，适合业务决策场景
销售流程涉及将潜在客户转化为实际客户，并向现有客户推销更多产品，其优化是B2B企业成功的关键。本文提出一种系统性销售优化与商业AI方法——因果预测优化与生成（Causal Predictive Optimization and Generation），包含三层架构：1）因果机器学习预测层；2）带约束的优化与上下文强化学习优化层；3）生成式AI与反馈循环的服务层。我们在LinkedIn上实现了该系统的部署，相比旧有系统取得显著成效，并分享了对本领域具有普遍适用性的经验与洞见。
原文摘要 · Abstract (English)
The sales process involves sales functions converting leads or opportunities to customers and selling more products to existing customers. The optimization of the sales process thus is key to success of any B2B business. In this work, we introduce a principled approach to sales optimization and business AI, namely the Causal Predictive Optimization and Generation, which includes three layers: 1) prediction layer with causal ML 2) optimization layer with constraint optimization and contextual bandit 3) serving layer with Generative AI and feedback-loop for system enhancement. We detail the implementation and deployment of the system in LinkedIn, showcasing significant wins over legacy systems and sharing learning and insight broadly applicable to this field.
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